Battery state-of-charge measurement and control model based on the Internet platform

Hong Xu, Shunli Wang, Chuangshi Qi, Huan Li, Long Zhou, Daniel Ioan Stroe, Kailong Liu, Lili Xia, Peng Yu, Weihao Shi, Weikang Ji, Wenhua Xu, Xianyong Xiao

Publikation: Bidrag til bog/antologi/rapport/konference proceedingBidrag til bog/antologiForskningpeer review

Abstract

Precise lithium-ion battery state-of-charge (SOC) is crucial for the battery measurement and control system. Therefore this chapter explores the lithium-ion battery SOC measurement and control model applied to the “Internet +” platform based on the previous analysis of lithium-ion operating characteristics. The traditional linearized Kalman filter is applied to lithium-ion batteries with strong nonlinearity, wherein the accuracy and versatility are found to be insufficient. The neural network algorithm directly starts from the external characteristics of the battery and does not need to consider the internal reaction mechanism. The mapping relationship between the external characteristic parameters of the battery and SOC can be obtained through data training. Therefore the SOC estimation method based on the neural network is the best solution.

OriginalsprogEngelsk
TitelState Estimation Strategies in Lithium-ion Battery Management Systems
RedaktørerShunli Wang, Kailong Liu, Yujie Wang, Daniel-Ioan Stroe, Carlos Fernandez, Josep M. Guerrero
Antal sider32
ForlagElsevier
Publikationsdato2023
Sider141-172
Kapitel8
ISBN (Trykt)978-0-443-16160-5
DOI
StatusUdgivet - 2023

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© 2023 Elsevier Inc. All rights reserved.

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